Management Information Systems

MIST 315- M01

Internet Research Project

Question 1

Data science is the use of scientific methods, algorithms, processes and systems to obtain knowledge and understanding through use of data in a wide range of ways both unstructured and structured data. Business analytics is an iterative practice, methodological exploration of an institutions data emphasizing on statistical analysis (El Bousty, Elasikri Dani, Karimi,Bendaoud, & Kabrane, 2018). It is used by companies in decision making processes. It can also be used to automate and optimize business process. Business analytics is classified into business intelligence and statistical analysis. Business intelligence involves examination of historical data to obtain a sense of a department or an employee performed during a certain period. On the other hand statistical analysis involves performing predictive analytics by use of statistical algorithms to historical information .The historical data is then used to predict about the future performance of a product or a service. It also means use of techniques such as cluster analysis to obtain similarities between groups that targets marketing campaign.

Data science involves an understanding of data and trying to understand the characteristics that can be drawn from the data. The approach in data science if problem based .Business analytics involves conversion of data in making decisions .For instance in data science one can obtain records of credit cards of a customer and develop a model that predict good customers when a bank wants to offer a loan. Business analytics in this scenario examines the data out of it and makes a decision on which customers are good. Data science focuses on accuracy while business analytics tries to figure out what is useful to a client.

Question 2

EPITA Graduate School of computer science in France is a good school for data analysts. The University offers a program provides a foundation in machine learning and programming along with practical experience. The University provides a combination of technical classes in data mining and distributed systems that are joined with important business to prepare professionals that are highly valued in the market. Important course units offered are introduction to Data science with Python, applied statistics for engineers, advanced machine learning, optimization for data science, digital marketing and social media strategy, deep learning, data visualization, social media and web analytics, predictive analytics and data mining, statistical models and regression.

Florida University offers a degree in Business analytics .The course is of great importance in most organizations so that they can maintain a competitive advantage. Business intelligence includes application and practice for data collection, integration analysis’s, information technologies and presentation of business data information. It consists of data analytics’, data mining, data visualization and OLAP techniques that evaluate big data. Courses offered consists of data analytics and mining for business, data management in business analytics, foundational concepts for business analytics, forecasting revenue management and pricing, programming for analytics, probabilistic optimizing for analytics, quantitative methods in Business Analytics and Analytics Capstone Lab

Question 3

Data science can be used by a business to mitigate risk and fraud; this is because data scientist can identify data that stands out. Data scientists can create a statistical network, path and big data methodologies for predicting fraud propensity models. The data can be used to create alerts and help in ensuring a timely response where unusual data can be recognized. Additionally data science is used to deliver relevant products in a business. An organisation can identify markets that their products can perform best(El Bousty, Elasikri Dani, Karimi,Bendaoud, & Kabrane, 2018). This enables a business deliver their products at the right time and develop products that suits their customer demand. It also offers customized customer experience, because it helps he sales and marketing team understand their clients. Through this a company is able to obtain the best customer experiences.

Business analytics is important to business as it helps in identifying critical products analysis, improvement of customer experience, up selling opportunities, simplifying inventory management and identifying competitive prices. Analysis of critical products enables alterations to be made to a location specific product that includes helping in determines trends associated in certain geographical areas. Improved customer services can track a customer question which in turn prevents a business from repeating the same mistakes and improve satisfaction of customers. Up selling on the other hand involves identification of the most prominent needs of a customer base. Inventory management is supported by business analytics as it helps predict which products are outdated and minimize losses(El Bousty, Elasikri Dani, Karimi,Bendaoud, & Kabrane, 2018). Competitive insights enables a firm make their prices the most competitive by tracking customer trends and price ranges that suits their customers. Business analytics also helps a business improve internal processes because they are able to understand what they can do efficiently and effectively in their organisations. It helps organisations understand problems that face the organisation and how to mitigate them.

Question 4

Currently there is a less data scientists and data analysts in the job market. The problem is expected to get worse within a certain period. Business analytics and intelligence will focus on usability and increase use of natural languages that enables data user’s obtain data and formulate reports without the understanding of algorithms.Through this increased efficiencies and further adoption by companies .In the future there will be increased reliance of large data networks. Most companies tend to value vast data stores. Consumer data available in cloud can help a company supplement their existing customer data hence enabling the company provide a personal services and creation new services to meet the other customer needs. Machine learning is bound to improve, this will make companies harness their power and create a new service that provides them with a competitive advantage. Machine learning is bound to take over customer services in the future. Managing of data will become more complex. Additionally interconnectivity will be crucial to success. The over reliance on new organizational tools for data analysis and business intelligence and accessibility to external data stores, IoT services and networks interconnectivity will be crucial for a whole coverage data analytics machine. For competitiveness of a business, it is critical for a business to invent a plan to secure educated talents so that they can plan well for strategic investments. There is also need of creating process strategies for the maintenance of data in all the systems.

Data analysts should pay attention to “with the skills to understand and make decisions based on the analysis of big data”. Strong educational background will be required with an in-depth knowledge necessary to become a data scientist’s programming is essential for a data scientist. A data scientist should be conversant with python coding, hadoop plat form and SQL database coding and Artificial intelligence. Masters degree is favorable for a data analyst student.

Question 5

Sisense is used in Business intelligence. It offers a drag and drop interface that can be used to combine all data and display them in a dashboard. Sisense can be used to visualize and analyze large data sets. It is also user friendly and can be used to explore and drill down into large and complex data sets so as to obtain answers to questions. Sisense users not need to maintain or use loads of hardware and software in the in clip engines and uses computing resources optimally. Sisense features include attractive visualization, centralized data hub, data mash, interactive dashboards and interaction with web portals. When pricing the vendor evaluates their needs and offers a customer quote and one is assured if a technical maintenance support.

For data scientists R analysis offers a wide range of factors such as linear and non –linear programming, clustering, time series analysis, easy extensibility and interfaces so as to prevent programming languages and sizeable shared code package repository. R has a strong Integrated Development Environment that is accessible in R studio and is accessible in a number of scripting languages. A student who requires a career in data science should require more than passing the familiarity with R.

Looker is used in Business Intelligence; it requires one to have SQL knowledge so as to make it more effective. It provides interactive puzzles which makes analytics a fun of activity. It analyzes SQL and information on the web. It also accommodates more than 25 varied data that includes Goggles Big Query, Vertical and Hive. Characteristics of looker include collaboration tools, drag and drop mechanism, customizable and exportable graphs, charts and reports for API integration.

References

El Bousty, H., Elasikri, M., Dani, H., Karimi, K., Bendaoud, K., & Kabrane, M. (2018, June). Investigating Business Intelligence in the era of Big Data: concepts, benefits and challenges. In Proceedings of the Fourth International Conference on Engineering & MIS 2018 (p. 25). ACM.

Stephan, P. E., & Levin, S. G. (2014). Striking the mother lode in science: The importance of age, place, and time. Oxford University Press, USA.

Mauger, J. W. (2018). Comment on the Importance of Data Transparency, Openness, and Reproducibility in Dissolution Science and Technology.